Load libraries (packages)

library("respR") ## respirometry/slope analysis
library("tidyverse") ## data manipulation

Set working directory

setwd("[PATH TO DIRECTORY]")

System1 - Dell

Importing data from firesting for resting

preexperiment_date <- "13 April 2023 11 34AM/All"
postexperiment_date <- "13 April 2023 06 03PM/All"

##--- last fish run in trial ---##
experiment_date <- "13 April 2023 02 34PM/Oxygen"
experiment_date2 <- "13 April 2023 02 34PM/All"

firesting <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19)

Cycle_1 <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Cycle_last <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_21.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

System2 - Asus

Importing data from firesting for resting

preexperiment_date_asus <- "13 April 2023 11 49AM/All"
postexperiment_date_asus <- "13 April 2023 05 24PM/All"

##--- last fish run in trial ---##
experiment_date_asus <- "13 April 2023 01 50PM/Oxygen"
experiment_date2_asus <- "13 April 2023 01 50PM/All"

firesting_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19)

Cycle_1_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Cycle_last_asus <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_21.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Chamber volumes

chamber1_dell = 0.04650
chamber2_dell = 0.04593
chamber3_dell = 0.04977
chamber4_dell = 0.04860 

chamber1_asus = 0.04565
chamber2_asus = 0.04573+0.00385
chamber3_asus = 0.04551+0.00322
chamber4_asus = 0.04791+0.00277

Date_tested="2023-04-13"
Clutch = "68" 
Male = "CARL218" 
Female = "CARL222"
Population = "Arlington reef"
Tank =163 
salinity =36 
Date_analysed = Sys.Date() 

Replicates

1

Enter specimen data

Replicate = 1 
mass = 0.0004418
chamber = "ch4" 
Swim = "good/good"
chamber_vol = chamber4_dell
system1 = "Dell"
Notes=""

##--- time of trail ---## 
experiment_mmr_date <- "13 April 2023 02 12PM/Oxygen"
experiment_mmr_date2 <- "13 April 2023 02 12PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] 2.370506e-05

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.002470306

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 1.92
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  6  8  9 11 13 14 15 17 18 19 21 24 25 26 27 29 30
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 1.84
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 1 rate(s) removed, 20 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 14 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   7    1     305.8826 -0.01477858 0.969      NA 2944   3177 14164.60
## 2:  11    1     395.6199 -0.01826401 0.991      NA 4921   5154 16324.92
## 3:  17    1     470.0189 -0.01903237 0.960      NA 7887   8120 19565.29
## 4:  18    1     435.5156 -0.01679579 0.982      NA 8380   8613 20104.51
## 5:  20    1     523.0705 -0.02009297 0.988      NA 9367   9601 21184.30
## 6:  21    1     491.1138 -0.01811991 0.979      NA 9862  10096 21725.00
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 14419.39 96.646 91.941 -0.01477858 -0.001103761   -0.01367482 -0.01367482
## 2: 16579.75 97.076 92.914 -0.01826401 -0.001637323   -0.01662669 -0.01662669
## 3: 19819.94 97.090 92.645 -0.01903237 -0.002437610   -0.01659476 -0.01659476
## 4: 20360.03 97.436 93.308 -0.01679579 -0.002570894   -0.01422490 -0.01422490
## 5: 21439.88 97.021 92.203 -0.02009297 -0.002837589   -0.01725539 -0.01725539
## 6: 21980.90 97.064 92.460 -0.01811991 -0.002971171   -0.01514874 -0.01514874
##    oxy.unit time.unit volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.0486 0.0004418   NA 36 30 1.013253 -0.1479779
## 2:     %Air       sec 0.0486 0.0004418   NA 36 30 1.013253 -0.1799206
## 3:     %Air       sec 0.0486 0.0004418   NA 36 30 1.013253 -0.1795751
## 4:     %Air       sec 0.0486 0.0004418   NA 36 30 1.013253 -0.1539304
## 5:     %Air       sec 0.0486 0.0004418   NA 36 30 1.013253 -0.1867238
## 6:     %Air       sec 0.0486 0.0004418   NA 36 30 1.013253 -0.1639274
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -334.9431          NA  mgO2/hr/kg   -334.9431
## 2:   -407.2444          NA  mgO2/hr/kg   -407.2444
## 3:   -406.4624          NA  mgO2/hr/kg   -406.4624
## 4:   -348.4164          NA  mgO2/hr/kg   -348.4164
## 5:   -422.6434          NA  mgO2/hr/kg   -422.6434
## 6:   -371.0444          NA  mgO2/hr/kg   -371.0444
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
68 1 CARL218 CARL222 Arlington reef 163 0.0004418 ch4 Dell 0.0486 2023-04-13 2024-06-26 good/good 36 30 391.1622 0.1728155 0.98

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 1.92
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  3  4  9 10 11 12 16 17 19 20 21 23 24 25 26 27 29 31 34
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.25
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     597.5698 -0.05217019 0.9978801      NA  71    126 9633.71
##   2:  NA    2     597.2068 -0.05213231 0.9977929      NA  72    127 9634.80
##   3:  NA    3     596.8770 -0.05209875 0.9977623      NA  70    125 9632.62
##   4:  NA    4     596.6662 -0.05207711 0.9977382      NA  69    124 9631.53
##   5:  NA    5     596.6585 -0.05207647 0.9977409      NA  68    123 9630.44
##  ---                                                                       
## 214:  NA  214     474.1138 -0.03933359 0.9937619      NA   5     60 9560.88
## 215:  NA  215     470.3180 -0.03893771 0.9941246      NA   4     59 9559.79
## 216:  NA  216     466.7156 -0.03856207 0.9939866      NA   3     58 9558.70
## 217:  NA  217     464.0401 -0.03828314 0.9935941      NA   2     57 9557.61
## 218:  NA  218     462.1889 -0.03809015 0.9933134      NA   1     56 9556.53
##      endtime    oxy endoxy        rate
##   1: 9693.71 94.922 91.894 -0.05217019
##   2: 9694.80 94.879 91.890 -0.05213231
##   3: 9692.62 95.009 91.944 -0.05209875
##   4: 9691.53 95.084 91.999 -0.05207711
##   5: 9690.44 95.139 92.052 -0.05207647
##  ---                                  
## 214: 9620.88 97.967 95.559 -0.03933359
## 215: 9619.79 97.974 95.649 -0.03893771
## 216: 9618.70 98.009 95.750 -0.03856207
## 217: 9617.61 98.066 95.822 -0.03828314
## 218: 9616.53 98.132 95.835 -0.03809015
## 
## Regressions : 218 | Results : 218 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 218 adjusted rate(s):
## Rate          : -0.05217019
## Adjustment    : 2.370506e-05
## Adjusted Rate : -0.0521939 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 0 rate(s) removed, 218 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 217 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     597.5698 -0.05217019 0.9978801      NA  71    126 9633.71
##    endtime    oxy endoxy        rate   adjustment rate.adjusted rate.input
## 1: 9693.71 94.922 91.894 -0.05217019 2.370506e-05    -0.0521939 -0.0521939
##    oxy.unit time.unit volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.0486 0.0004418   NA 36 30 1.013253 -0.5648002
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1278.407          NA  mgO2/hr/kg   -1278.407
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
68 1 CARL218 CARL222 Arlington reef 163 0.0004418 ch4 Dell 0.0486 2023-04-13 2024-06-26 good/good 36 30 391.1622 0.1728155 0.98 1278.407 0.5648002 0.9978801 887.2447 0.3919847

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 235 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

3

Enter specimen data

Replicate = 3 
mass = 0.0006633 
chamber = "ch2" 
Swim = "good/good"
chamber_vol = chamber2_dell
system1 = "Dell"
Notes=""

##--- time of trail ---## 
experiment_mmr_date <- "13 April 2023 02 34PM/Oxygen"
experiment_mmr_date2 <- "13 April 2023 02 34PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -3.697567e-05

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.003274644

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 1.92
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  6  8  9 11 13 14 15 17 18 19 21 24 25 26 27 29 30
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 1.84
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  13    1     546.1973 -0.02586434 0.994      NA 5909   6143 17404.97
## 2:  17    1     611.4436 -0.02626162 0.989      NA 7887   8120 19565.29
## 3:  18    1     628.4402 -0.02638851 0.992      NA 8380   8613 20104.51
## 4:  19    1     660.5001 -0.02724729 0.991      NA 8873   9106 20644.59
## 5:  20    1     615.4969 -0.02444218 0.994      NA 9367   9601 21184.30
## 6:  21    1     689.9515 -0.02725895 0.994      NA 9862  10096 21725.00
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 17660.28 96.115 89.463 -0.02586434 -0.002539652   -0.02332468 -0.02332468
## 2: 19819.94 97.219 90.557 -0.02626162 -0.003232198   -0.02302942 -0.02302942
## 3: 20360.03 97.617 90.988 -0.02638851 -0.003405225   -0.02298329 -0.02298329
## 4: 20899.39 97.653 90.890 -0.02724729 -0.003578273   -0.02366901 -0.02366901
## 5: 21439.88 97.392 91.318 -0.02444218 -0.003751442   -0.02069073 -0.02069073
## 6: 21980.90 97.320 90.886 -0.02725895 -0.003924855   -0.02333409 -0.02333409
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04593 0.0006633   NA 36 30 1.013253 -0.2385344
## 2:     %Air       sec 0.04593 0.0006633   NA 36 30 1.013253 -0.2355149
## 3:     %Air       sec 0.04593 0.0006633   NA 36 30 1.013253 -0.2350431
## 4:     %Air       sec 0.04593 0.0006633   NA 36 30 1.013253 -0.2420558
## 5:     %Air       sec 0.04593 0.0006633   NA 36 30 1.013253 -0.2115978
## 6:     %Air       sec 0.04593 0.0006633   NA 36 30 1.013253 -0.2386306
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -359.6177          NA  mgO2/hr/kg   -359.6177
## 2:   -355.0654          NA  mgO2/hr/kg   -355.0654
## 3:   -354.3541          NA  mgO2/hr/kg   -354.3541
## 4:   -364.9265          NA  mgO2/hr/kg   -364.9265
## 5:   -319.0077          NA  mgO2/hr/kg   -319.0077
## 6:   -359.7628          NA  mgO2/hr/kg   -359.7628
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple")  
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
68 3 CARL218 CARL222 Arlington reef 163 0.0006633 ch2 Dell 0.04593 2023-04-13 2024-06-26 good/good 36 30 358.7453 0.2379558 0.992

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 1.92
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  6  8  9 11 13 14 15 17 18 19 21 24 25 26 27 29 30
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.23
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow     time
##   1:  NA    1     942.9169 -0.07750740 0.9961596      NA  40     95 10923.29
##   2:  NA    2     942.2340 -0.07744729 0.9960949      NA  34     89 10916.76
##   3:  NA    3     941.9777 -0.07742206 0.9960436      NA  39     94 10922.20
##   4:  NA    4     941.6962 -0.07739558 0.9960007      NA  41     96 10924.38
##   5:  NA    5     941.6265 -0.07739207 0.9960418      NA  33     88 10915.68
##  ---                                                                        
## 216:  NA  216     591.7044 -0.04538008 0.9275727      NA   5     60 10885.01
## 217:  NA  217     579.9428 -0.04430190 0.9297208      NA   4     59 10883.91
## 218:  NA  218     568.4425 -0.04324741 0.9335795      NA   3     58 10882.82
## 219:  NA  219     556.6015 -0.04216174 0.9364279      NA   2     57 10881.74
## 220:  NA  220     543.8075 -0.04098881 0.9377728      NA   1     56 10880.66
##       endtime    oxy endoxy        rate
##   1: 10983.29 96.161 91.728 -0.07750740
##   2: 10976.76 96.690 92.182 -0.07744729
##   3: 10982.20 96.210 91.758 -0.07742206
##   4: 10984.38 96.140 91.697 -0.07739558
##   5: 10975.68 96.688 92.216 -0.07739207
##  ---                                   
## 216: 10945.01 97.627 94.563 -0.04538008
## 217: 10943.91 97.702 94.613 -0.04430190
## 218: 10942.82 97.669 94.738 -0.04324741
## 219: 10941.74 97.608 94.853 -0.04216174
## 220: 10940.66 97.562 94.979 -0.04098881
## 
## Regressions : 220 | Results : 220 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 220 adjusted rate(s):
## Rate          : -0.0775074
## Adjustment    : -3.697567e-05
## Adjusted Rate : -0.07747042 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 5 rate(s) removed, 215 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 214 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0   slope_b1       rsq density row endrow     time
## 1:  NA    1     942.9169 -0.0775074 0.9961596      NA  40     95 10923.29
##     endtime    oxy endoxy       rate    adjustment rate.adjusted  rate.input
## 1: 10983.29 96.161 91.728 -0.0775074 -3.697567e-05   -0.07747042 -0.07747042
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04593 0.0006633   NA 36 30 1.013253 -0.7922663
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1194.431          NA  mgO2/hr/kg   -1194.431
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
68 3 CARL218 CARL222 Arlington reef 163 0.0006633 ch2 Dell 0.04593 2023-04-13 2024-06-26 good/good 36 30 358.7453 0.2379558 0.992 1194.431 0.7922663 0.9961596 835.6861 0.5543106

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 236 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

4

Enter specimen data

Replicate = 4 
mass = 0.0003970
chamber = "ch1" 
Swim = "good/good"
chamber_vol = chamber1_dell
system1 = "Dell"
Notes=""

##--- time of trail ---## 
experiment_mmr_date <- "13 April 2023 12 59PM/Oxygen"
experiment_mmr_date2 <- "13 April 2023 12 59PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.001729053

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.004820875

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 1.92
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  6  8  9 11 13 14 15 17 18 19 21 24 25 26 27 29 30
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 1.84
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE)  
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  14    1     468.7177 -0.02069270 0.992      NA 6404   6638 17945.30
## 2:  16    1     501.1961 -0.02123256 0.994      NA 7392   7626 19024.58
## 3:  17    1     529.7155 -0.02208961 0.998      NA 7887   8120 19565.29
## 4:  18    1     495.5050 -0.01979115 0.998      NA 8380   8613 20104.51
## 5:  19    1     485.5071 -0.01878655 0.998      NA 8873   9106 20644.59
## 6:  20    1     560.0232 -0.02181629 0.999      NA 9367   9601 21184.30
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 18201.00 97.317 91.789 -0.02069270 -0.004284491   -0.01640821 -0.01640821
## 2: 19280.36 97.281 91.900 -0.02123256 -0.004614958   -0.01661761 -0.01661761
## 3: 19819.94 97.502 91.736 -0.02208961 -0.004780341   -0.01730927 -0.01730927
## 4: 20360.03 97.707 92.418 -0.01979115 -0.004945573   -0.01484558 -0.01484558
## 5: 20899.39 97.838 92.923 -0.01878655 -0.005110826   -0.01367572 -0.01367572
## 6: 21439.88 97.784 92.193 -0.02181629 -0.005276194   -0.01654010 -0.01654010
##    oxy.unit time.unit volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.0465 0.000397   NA 36 30 1.013253 -0.1698842
## 2:     %Air       sec 0.0465 0.000397   NA 36 30 1.013253 -0.1720522
## 3:     %Air       sec 0.0465 0.000397   NA 36 30 1.013253 -0.1792135
## 4:     %Air       sec 0.0465 0.000397   NA 36 30 1.013253 -0.1537053
## 5:     %Air       sec 0.0465 0.000397   NA 36 30 1.013253 -0.1415931
## 6:     %Air       sec 0.0465 0.000397   NA 36 30 1.013253 -0.1712497
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -427.9199          NA  mgO2/hr/kg   -427.9199
## 2:   -433.3809          NA  mgO2/hr/kg   -433.3809
## 3:   -451.4193          NA  mgO2/hr/kg   -451.4193
## 4:   -387.1670          NA  mgO2/hr/kg   -387.1670
## 5:   -356.6576          NA  mgO2/hr/kg   -356.6576
## 6:   -431.3595          NA  mgO2/hr/kg   -431.3595
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple")  
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
68 4 CARL218 CARL222 Arlington reef 163 0.000397 ch1 Dell 0.0465 2023-04-13 2024-06-26 good/good 36 30 426.2493 0.169221 0.9962

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 1.92
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row+175, 
              to = cycle1.end.row, #custom
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  4  5  6  7  8 10 12 14 15 16 18 20 21 23 24 25 27 28
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.33
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##     rep rank intercept_b0    slope_b1       rsq density row endrow    time
##  1:  NA    1     362.7170 -0.05056191 0.9959885      NA   1     54 5359.22
##  2:  NA    2     362.3250 -0.05048938 0.9960242      NA   2     55 5360.31
##  3:  NA    3     361.9063 -0.05041193 0.9960788      NA   3     56 5361.63
##  4:  NA    4     360.5861 -0.05016711 0.9960391      NA   4     57 5362.72
##  5:  NA    5     358.9604 -0.04986551 0.9957863      NA   5     58 5363.80
## ---                                                                       
## 32:  NA   32     309.9576 -0.04080734 0.9929120      NA  32     85 5394.71
## 33:  NA   33     306.6944 -0.04020622 0.9938595      NA  33     86 5395.82
## 34:  NA   34     304.5274 -0.03980712 0.9942764      NA  34     87 5396.91
## 35:  NA   35     303.4276 -0.03960462 0.9943726      NA  35     88 5397.99
## 36:  NA   36     302.6330 -0.03945816 0.9942496      NA  36     89 5399.09
##     endtime    oxy endoxy        rate
##  1: 5419.22 91.833 88.723 -0.05056191
##  2: 5420.31 91.753 88.649 -0.05048938
##  3: 5421.63 91.689 88.580 -0.05041193
##  4: 5422.72 91.641 88.606 -0.05016711
##  5: 5423.80 91.568 88.601 -0.04986551
## ---                                  
## 32: 5454.71 90.033 87.456 -0.04080734
## 33: 5455.82 89.979 87.442 -0.04020622
## 34: 5456.91 89.847 87.373 -0.03980712
## 35: 5457.99 89.726 87.300 -0.03960462
## 36: 5459.09 89.629 87.278 -0.03945816
## 
## Regressions : 36 | Results : 36 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 36 adjusted rate(s):
## Rate          : -0.05056191
## Adjustment    : -0.001729053
## Adjusted Rate : -0.04883286 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 0 rate(s) removed, 36 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 35 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1      362.717 -0.05056191 0.9959885      NA   1     54 5359.22
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 5419.22 91.833 88.723 -0.05056191 -0.001729053   -0.04883286 -0.04883286
##    oxy.unit time.unit volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.0465 0.000397   NA 36 30 1.013253 -0.5055964
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1273.542          NA  mgO2/hr/kg   -1273.542
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
68 4 CARL218 CARL222 Arlington reef 163 0.000397 ch1 Dell 0.0465 2023-04-13 2024-06-26 good/good 36 30 426.2493 0.169221 0.9962 1273.542 0.5055964 0.9959885 847.2931 0.3363754

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 237 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

5

Enter specimen data

Replicate = 5 
mass = 0.0006690
chamber = "ch4" 
Swim = "good/good"
chamber_vol = chamber4_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "13 April 2023 01 15PM/Oxygen"
experiment_mmr_date2_asus <- "13 April 2023 01 15PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.000953862

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.0001487799

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 5.32
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  6  7  8  9 11 13 14 15 16 17 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 5.32
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   1    1     284.6191 -0.02552010 0.992      NA   34    223  7374.84
## 2:   3    1     300.0943 -0.02400127 0.982      NA  835   1024  8455.58
## 3:   4    1     265.6396 -0.01870970 0.989      NA 1235   1424  8995.01
## 4:   6    1     327.0009 -0.02284366 0.979      NA 2024   2157 10073.98
## 5:  14    1     443.2819 -0.02408024 0.992      NA 5049   5239 14395.23
## 6:  15    1     457.2083 -0.02413165 0.993      NA 5450   5640 14935.47
##     endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1:  7629.75 96.516 90.045 -0.02552010 -7.454818e-04   -0.02477462 -0.02477462
## 2:  8710.65 97.094 90.523 -0.02400127 -6.373216e-04   -0.02336395 -0.02336395
## 3:  9249.84 97.161 92.361 -0.01870970 -5.833516e-04   -0.01812634 -0.01812634
## 4: 10330.84 97.228 91.588 -0.02284366 -4.752750e-04   -0.02236838 -0.02236838
## 5: 14651.31 96.625 90.145 -0.02408024 -4.287636e-05   -0.02403736 -0.02403736
## 6: 15191.46 96.565 90.154 -0.02413165  1.118223e-05   -0.02414283 -0.02414283
##    oxy.unit time.unit  volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.05068 0.000669   NA 36 30 1.013253 -0.2795648
## 2:     %Air       sec 0.05068 0.000669   NA 36 30 1.013253 -0.2636463
## 3:     %Air       sec 0.05068 0.000669   NA 36 30 1.013253 -0.2045435
## 4:     %Air       sec 0.05068 0.000669   NA 36 30 1.013253 -0.2524120
## 5:     %Air       sec 0.05068 0.000669   NA 36 30 1.013253 -0.2712453
## 6:     %Air       sec 0.05068 0.000669   NA 36 30 1.013253 -0.2724354
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -417.8846          NA  mgO2/hr/kg   -417.8846
## 2:   -394.0902          NA  mgO2/hr/kg   -394.0902
## 3:   -305.7451          NA  mgO2/hr/kg   -305.7451
## 4:   -377.2975          NA  mgO2/hr/kg   -377.2975
## 5:   -405.4489          NA  mgO2/hr/kg   -405.4489
## 6:   -407.2279          NA  mgO2/hr/kg   -407.2279
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
68 5 CARL218 CARL222 Arlington reef 163 0.000669 ch4 Asus 0.05068 2023-04-13 2024-06-26 good/good 36 30 400.3898 0.2678608 0.9876

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.54
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row+140, 
              to = cycle1.end.row, # custom
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  3  5  6  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.42
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##     rep rank intercept_b0    slope_b1       rsq density row endrow    time
##  1:  NA    1     348.9043 -0.04777199 0.9940144      NA   1     46 5451.22
##  2:  NA    2     345.5246 -0.04715523 0.9932431      NA   2     47 5452.58
##  3:  NA    3     341.3173 -0.04638724 0.9909287      NA   3     48 5453.95
##  4:  NA    4     337.6039 -0.04570930 0.9882757      NA   4     49 5455.28
##  5:  NA    5     333.1374 -0.04489454 0.9858512      NA   5     50 5456.62
## ---                                                                       
## 34:  NA   34     297.7305 -0.03845581 0.9812379      NA  23     68 5481.02
## 35:  NA   35     297.2474 -0.03836726 0.9815581      NA  22     67 5479.68
## 36:  NA   36     297.1856 -0.03835341 0.9811266      NA  19     64 5475.57
## 37:  NA   37     296.3973 -0.03821135 0.9818326      NA  20     65 5476.92
## 38:  NA   38     296.1207 -0.03816210 0.9820575      NA  21     66 5478.29
##     endtime    oxy endoxy        rate
##  1: 5511.22 88.733 85.709 -0.04777199
##  2: 5512.58 88.559 85.742 -0.04715523
##  3: 5513.95 88.455 85.792 -0.04638724
##  4: 5515.28 88.331 85.760 -0.04570930
##  5: 5516.62 88.319 85.720 -0.04489454
## ---                                  
## 34: 5541.02 87.077 84.520 -0.03845581
## 35: 5539.68 87.071 84.584 -0.03836726
## 36: 5535.57 87.463 84.749 -0.03835341
## 37: 5536.92 87.321 84.722 -0.03821135
## 38: 5538.29 87.225 84.660 -0.03816210
## 
## Regressions : 38 | Results : 38 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 38 adjusted rate(s):
## Rate          : -0.04777199
## Adjustment    : -0.000953862
## Adjusted Rate : -0.04681813 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 0 rate(s) removed, 38 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 37 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     348.9043 -0.04777199 0.9940144      NA   1     46 5451.22
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 5511.22 88.733 85.709 -0.04777199 -0.000953862   -0.04681813 -0.04681813
##    oxy.unit time.unit  volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.05068 0.000669   NA 36 30 1.013253 -0.5283109
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -789.7023          NA  mgO2/hr/kg   -789.7023
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
68 5 CARL218 CARL222 Arlington reef 163 0.000669 ch4 Asus 0.05068 2023-04-13 2024-06-26 good/good 36 30 400.3898 0.2678608 0.9876 789.7023 0.5283109 0.9940144 389.3125 0.2604501
### Expor ting data
resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 238 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

6

Enter specimen data

Replicate = 6 
mass = 0.0004899
chamber = "ch3" 
Swim = "good/good"
chamber_vol = chamber3_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "13 April 2023 01 27PM/Oxygen"
experiment_mmr_date2_asus <- "13 April 2023 01 27PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.0002608429

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.003242443

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 5.32
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  6  7  8  9 11 13 14 15 16 17 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 5.32
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   6    1     225.0296 -0.01280567 0.979      NA 2024   2149 10073.98
## 2:   8    1     236.3745 -0.01257099 0.972      NA 2646   2828 11154.89
## 3:   9    1     251.4158 -0.01325632 0.980      NA 3047   3229 11695.60
## 4:  10    1     247.2336 -0.01233261 0.988      NA 3447   3629 12234.81
## 5:  14    1     302.9564 -0.01435974 0.991      NA 5049   5231 14395.23
## 6:  20    1     327.1181 -0.01307095 0.984      NA 7453   7635 17634.93
##     endtime    oxy endoxy        rate   adjustment rate.adjusted   rate.input
## 1: 10320.01 95.857 92.687 -0.01280567 -0.002031270  -0.010774399 -0.010774399
## 2: 11400.15 95.848 92.638 -0.01257099 -0.002431729  -0.010139256 -0.010139256
## 3: 11941.10 96.176 92.749 -0.01325632 -0.002632169  -0.010624153 -0.010624153
## 4: 12480.05 96.172 93.041 -0.01233261 -0.002831961  -0.009500648 -0.009500648
## 5: 14640.53 96.055 92.382 -0.01435974 -0.003632656  -0.010727081 -0.010727081
## 6: 17880.23 96.506 93.227 -0.01307095 -0.004833339  -0.008237612 -0.008237612
##    oxy.unit time.unit  volume      mass area  S  t        P    rate.abs
## 1:     %Air       sec 0.04873 0.0004899   NA 36 30 1.013253 -0.11690371
## 2:     %Air       sec 0.04873 0.0004899   NA 36 30 1.013253 -0.11001232
## 3:     %Air       sec 0.04873 0.0004899   NA 36 30 1.013253 -0.11527352
## 4:     %Air       sec 0.04873 0.0004899   NA 36 30 1.013253 -0.10308334
## 5:     %Air       sec 0.04873 0.0004899   NA 36 30 1.013253 -0.11639030
## 6:     %Air       sec 0.04873 0.0004899   NA 36 30 1.013253 -0.08937923
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -238.6277          NA  mgO2/hr/kg   -238.6277
## 2:   -224.5608          NA  mgO2/hr/kg   -224.5608
## 3:   -235.3001          NA  mgO2/hr/kg   -235.3001
## 4:   -210.4171          NA  mgO2/hr/kg   -210.4171
## 5:   -237.5797          NA  mgO2/hr/kg   -237.5797
## 6:   -182.4438          NA  mgO2/hr/kg   -182.4438
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
68 6 CARL218 CARL222 Arlington reef 163 0.0004899 ch3 Asus 0.04873 2023-04-13 2024-06-26 good/good 36 30 229.2971 0.1123326 0.982

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.54
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  9 10 11 12 13 14 15 16 17 18 19 20 21
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.48
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     377.5253 -0.04606571 0.9837356      NA 177    222 6185.24
##   2:  NA    2     377.4441 -0.04605243 0.9837061      NA 178    223 6186.60
##   3:  NA    3     377.0383 -0.04598984 0.9831877      NA 173    218 6179.83
##   4:  NA    4     376.6596 -0.04592998 0.9828666      NA 172    217 6178.49
##   5:  NA    5     375.8523 -0.04579829 0.9825731      NA 174    219 6181.19
##  ---                                                                       
## 174:  NA  174     158.0724 -0.01017860 0.9167527      NA  35     80 5993.35
## 175:  NA  175     157.7408 -0.01012391 0.9184417      NA  30     75 5986.63
## 176:  NA  176     157.4621 -0.01007757 0.9145776      NA  33     78 5990.64
## 177:  NA  177     157.2542 -0.01004294 0.9142750      NA  32     77 5989.31
## 178:  NA  178     156.6384 -0.00994054 0.9133940      NA  31     76 5987.97
##      endtime    oxy endoxy        rate
##   1: 6245.24 92.450 89.863 -0.04606571
##   2: 6246.60 92.555 89.805 -0.04605243
##   3: 6239.83 92.696 90.212 -0.04598984
##   4: 6238.49 92.696 90.183 -0.04592998
##   5: 6241.19 92.772 90.137 -0.04579829
##  ---                                  
## 174: 6053.35 97.066 96.499 -0.01017860
## 175: 6046.63 97.111 96.608 -0.01012391
## 176: 6050.64 97.102 96.468 -0.01007757
## 177: 6049.31 97.100 96.467 -0.01004294
## 178: 6047.97 97.145 96.579 -0.00994054
## 
## Regressions : 178 | Results : 178 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 178 adjusted rate(s):
## Rate          : -0.04606571
## Adjustment    : -0.0002608429
## Adjusted Rate : -0.04580486 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 64 rate(s) removed, 114 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 113 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     377.5253 -0.04606571 0.9837356      NA 177    222 6185.24
##    endtime   oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1: 6245.24 92.45 89.863 -0.04606571 -0.0002608429   -0.04580486 -0.04580486
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04873 0.0004899   NA 36 30 1.013253 -0.4969891
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:    -1014.47          NA  mgO2/hr/kg    -1014.47
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
68 6 CARL218 CARL222 Arlington reef 163 0.0004899 ch3 Asus 0.04873 2023-04-13 2024-06-26 good/good 36 30 229.2971 0.1123326 0.982 1014.47 0.4969891 0.9837356 785.1734 0.3846564
### Expor ting data
resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 239 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

7

Enter specimen data

Replicate = 7 
mass = 0.0005580 
chamber = "ch2" 
Swim = "good/good"
chamber_vol = chamber2_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "13 April 2023 01 39PM/Oxygen"
experiment_mmr_date2_asus <- "13 April 2023 01 39PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] 0.0005536763

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.002782402

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 5.32
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  6  7  8  9 11 13 14 15 16 17 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 5.32
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   8    1     393.3084 -0.02648319 0.988      NA 2646   2828 11154.89
## 2:   9    1     423.1968 -0.02780863 0.995      NA 3047   3229 11695.60
## 3:  10    1     420.0446 -0.02633447 0.999      NA 3447   3629 12234.81
## 4:  13    1     458.5061 -0.02604118 0.996      NA 4648   4830 13854.66
## 5:  14    1     474.9722 -0.02625677 0.987      NA 5049   5231 14395.23
## 6:  16    1     534.9506 -0.02823539 0.993      NA 5850   6032 15474.42
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 11400.15 97.728 90.742 -0.02648319 -0.001875304   -0.02460789 -0.02460789
## 2: 11941.10 97.707 91.160 -0.02780863 -0.002099574   -0.02570906 -0.02570906
## 3: 12480.05 97.783 91.318 -0.02633447 -0.002323118   -0.02401135 -0.02401135
## 4: 14100.34 97.724 91.216 -0.02604118 -0.002994924   -0.02304626 -0.02304626
## 5: 14640.53 97.228 90.093 -0.02625677 -0.003219008   -0.02303776 -0.02303776
## 6: 15719.70 97.821 90.691 -0.02823539 -0.003666519   -0.02456887 -0.02456887
##    oxy.unit time.unit  volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04958 0.000558   NA 36 30 1.013253 -0.2716562
## 2:     %Air       sec 0.04958 0.000558   NA 36 30 1.013253 -0.2838125
## 3:     %Air       sec 0.04958 0.000558   NA 36 30 1.013253 -0.2650708
## 4:     %Air       sec 0.04958 0.000558   NA 36 30 1.013253 -0.2544168
## 5:     %Air       sec 0.04958 0.000558   NA 36 30 1.013253 -0.2543230
## 6:     %Air       sec 0.04958 0.000558   NA 36 30 1.013253 -0.2712255
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -486.8391          NA  mgO2/hr/kg   -486.8391
## 2:   -508.6246          NA  mgO2/hr/kg   -508.6246
## 3:   -475.0373          NA  mgO2/hr/kg   -475.0373
## 4:   -455.9441          NA  mgO2/hr/kg   -455.9441
## 5:   -455.7759          NA  mgO2/hr/kg   -455.7759
## 6:   -486.0672          NA  mgO2/hr/kg   -486.0672
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
68 7 CARL218 CARL222 Arlington reef 163 0.000558 ch2 Asus 0.04958 2023-04-13 2024-06-26 good/good 36 30 482.5025 0.2692364 0.9942

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.55
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  6  7  8 11 12 13 14 15 16 17 18 20 21 22 23 24 26
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.55
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     476.1687 -0.05628857 0.9945543      NA  13     58 6714.30
##   2:  NA    2     475.5194 -0.05619156 0.9943061      NA  14     59 6715.66
##   3:  NA    3     475.3140 -0.05616256 0.9942390      NA  12     57 6712.93
##   4:  NA    4     474.4222 -0.05602813 0.9938592      NA  15     60 6717.01
##   5:  NA    5     473.2167 -0.05585201 0.9936924      NA  11     56 6711.58
##  ---                                                                       
## 174:  NA  174     240.4966 -0.02196510 0.9223394      NA 174    219 6931.44
## 175:  NA  175     234.6936 -0.02113142 0.9135290      NA 175    220 6932.78
## 176:  NA  176     229.8617 -0.02043751 0.9073327      NA 176    221 6934.14
## 177:  NA  177     226.3619 -0.01993520 0.9048159      NA 177    222 6935.49
## 178:  NA  178     223.0682 -0.01946260 0.9025891      NA 178    223 6936.84
##      endtime    oxy endoxy        rate
##   1: 6774.30 98.124 94.936 -0.05628857
##   2: 6775.66 98.102 94.917 -0.05619156
##   3: 6772.93 98.185 94.936 -0.05616256
##   4: 6777.01 98.035 94.873 -0.05602813
##   5: 6771.58 98.213 94.941 -0.05585201
##  ---                                  
## 174: 6991.44 88.436 87.191 -0.02196510
## 175: 6992.78 88.381 87.133 -0.02113142
## 176: 6994.14 88.320 87.075 -0.02043751
## 177: 6995.49 88.265 86.987 -0.01993520
## 178: 6996.84 88.219 86.963 -0.01946260
## 
## Regressions : 178 | Results : 178 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 178 adjusted rate(s):
## Rate          : -0.05628857
## Adjustment    : 0.0005536763
## Adjusted Rate : -0.05684225 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 5 rate(s) removed, 173 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 172 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow   time
## 1:  NA    1     476.1687 -0.05628857 0.9945543      NA  13     58 6714.3
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1:  6774.3 98.124 94.936 -0.05628857 0.0005536763   -0.05684225 -0.05684225
##    oxy.unit time.unit  volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04958 0.000558   NA 36 30 1.013253 -0.6275042
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1124.559          NA  mgO2/hr/kg   -1124.559
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
68 7 CARL218 CARL222 Arlington reef 163 0.000558 ch2 Asus 0.04958 2023-04-13 2024-06-26 good/good 36 30 482.5025 0.2692364 0.9942 1124.559 0.6275042 0.9945543 642.057 0.3582678
### Expor ting data
resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 240 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

8

Enter specimen data

Replicate = 8 
mass = 0.0003641 
chamber = "ch1" 
Swim = "good/good"
chamber_vol = chamber1_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "13 April 2023 01 50PM/Oxygen"
experiment_mmr_date2_asus <- "13 April 2023 01 50PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.0004981241

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.003085365

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 5.32
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  6  7  8  9 11 13 14 15 16 17 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 5.32
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 8 rate(s) removed, 13 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 7 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0     slope_b1   rsq density  row endrow     time
## 1:  12    1     167.1063 -0.005237533 0.952      NA 4248   4430 13315.31
## 2:  15    1     171.0555 -0.004909408 0.951      NA 5450   5632 14935.47
## 3:  16    1     185.1890 -0.005633264 0.951      NA 5850   6032 15474.42
## 4:  18    1     191.7177 -0.005682465 0.961      NA 6651   6833 16554.27
## 5:  19    1     179.8869 -0.004800513 0.952      NA 7052   7234 17094.47
## 6:  20    1     187.3045 -0.005067284 0.978      NA 7453   7635 17634.93
##     endtime    oxy endoxy         rate   adjustment rate.adjusted    rate.input
## 1: 13561.05 97.126 95.895 -0.005237533 -0.003076739 -0.0021607936 -0.0021607936
## 2: 15180.61 97.674 96.311 -0.004909408 -0.003597680 -0.0013117284 -0.0013117284
## 3: 15719.70 97.875 96.491 -0.005633264 -0.003771027 -0.0018622369 -0.0018622369
## 4: 16799.55 97.529 96.113 -0.005682465 -0.004118302 -0.0015641626 -0.0015641626
## 5: 17339.77 97.731 96.515 -0.004800513 -0.004292032 -0.0005084817 -0.0005084817
## 6: 17880.23 97.915 96.638 -0.005067284 -0.004465842 -0.0006014423 -0.0006014423
##    oxy.unit time.unit  volume      mass area  S  t        P     rate.abs
## 1:     %Air       sec 0.04565 0.0003641   NA 36 30 1.013253 -0.021963062
## 2:     %Air       sec 0.04565 0.0003641   NA 36 30 1.013253 -0.013332867
## 3:     %Air       sec 0.04565 0.0003641   NA 36 30 1.013253 -0.018928428
## 4:     %Air       sec 0.04565 0.0003641   NA 36 30 1.013253 -0.015898696
## 5:     %Air       sec 0.04565 0.0003641   NA 36 30 1.013253 -0.005168386
## 6:     %Air       sec 0.04565 0.0003641   NA 36 30 1.013253 -0.006113270
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -60.32151          NA  mgO2/hr/kg   -60.32151
## 2:   -36.61869          NA  mgO2/hr/kg   -36.61869
## 3:   -51.98689          NA  mgO2/hr/kg   -51.98689
## 4:   -43.66574          NA  mgO2/hr/kg   -43.66574
## 5:   -14.19496          NA  mgO2/hr/kg   -14.19496
## 6:   -16.79008          NA  mgO2/hr/kg   -16.79008
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
68 8 CARL218 CARL222 Arlington reef 163 0.0003641 ch1 Asus 0.04565 2023-04-13 2024-06-26 good/good 36 30 41.87659 0.0152473 0.9586

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 5.32
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  3  4  5  7  8  9 10 11 12 13 14 15 16 17 18 19 20 22 23
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.39
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0     slope_b1       rsq density row endrow    time
##   1:  NA    1     253.9795 -0.021100841 0.9881483      NA  25     70 7363.42
##   2:  NA    2     253.7203 -0.021065524 0.9878767      NA  26     71 7364.76
##   3:  NA    3     252.9809 -0.020966178 0.9866622      NA  24     69 7362.06
##   4:  NA    4     252.5921 -0.020912330 0.9859494      NA  28     73 7367.42
##   5:  NA    5     252.4619 -0.020894586 0.9858619      NA  29     74 7368.78
##  ---                                                                        
## 175:  NA  175     156.7766 -0.008026093 0.9271842      NA  81    126 7438.90
## 176:  NA  176     156.6956 -0.008015034 0.9273537      NA  80    125 7437.56
## 177:  NA  177     156.5514 -0.007998644 0.9008004      NA 108    153 7475.29
## 178:  NA  178     156.1262 -0.007940850 0.8864659      NA  99    144 7463.17
## 179:  NA  179     155.7400 -0.007889242 0.8845472      NA 100    145 7464.52
##      endtime    oxy endoxy         rate
##   1: 7423.42 98.518 97.377 -0.021100841
##   2: 7424.76 98.556 97.365 -0.021065524
##   3: 7422.06 98.513 97.281 -0.020966178
##   4: 7427.42 98.490 97.297 -0.020912330
##   5: 7428.78 98.489 97.267 -0.020894586
##  ---                                   
## 175: 7498.90 97.111 96.552 -0.008026093
## 176: 7497.56 97.145 96.588 -0.008015034
## 177: 7535.29 96.809 96.281 -0.007998644
## 178: 7523.17 96.920 96.403 -0.007940850
## 179: 7524.52 96.854 96.402 -0.007889242
## 
## Regressions : 179 | Results : 179 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 179 adjusted rate(s):
## Rate          : -0.02110084
## Adjustment    : -0.0004981241
## Adjusted Rate : -0.02060272 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 78 rate(s) removed, 101 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 100 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     253.9795 -0.02110084 0.9881483      NA  25     70 7363.42
##    endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1: 7423.42 98.518 97.377 -0.02110084 -0.0004981241   -0.02060272 -0.02060272
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04565 0.0003641   NA 36 30 1.013253 -0.2094132
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:    -575.153          NA  mgO2/hr/kg    -575.153
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
68 8 CARL218 CARL222 Arlington reef 163 0.0003641 ch1 Asus 0.04565 2023-04-13 2024-06-26 good/good 36 30 41.87659 0.0152473 0.9586 575.153 0.2094132 0.9881483 533.2765 0.194166
### Expor ting data
resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 241 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)